1. Field of the Invention
The invention relates to a method, a software program and a system for processing MRT data of the human brain of a patient, wherein three-dimensional MRT data resolved in voxels of the patient's brain and the brains of a normative database of a plurality of neurologically healthy human individuals are available.
The investigation of magnetic resonance tomography data (MRT) of human brains with degenerative symptoms such as lesions is an important tool in the medical examination of human brains. Corresponding automated or semiautomated methods that permit statistical examinations of three-dimensional MRT data are known. Their results provide the diagnosing physician with criteria for his diagnosis.
2. Description of Related Art
Various approaches are followed for corresponding automated, or partially automated, raw-data-based examination methods. A first known method is to perform a voxel-based morphometry of the brain of the patient. The voxel-based morphometry consists of classifying the individual volume elements (voxels) of the MRT data of the brain into categories of grey matter or respectively substance (GM), white matter or respectively substance (WM), and cerebrospinal fluid or liquor (CSF) by means of a segmentation or respectively categorization algorithm. The different signals that these different substances in the brain emit as a reaction to the MRT scan are used to distinguish the different tissue classes. Typical voxel sizes of modern MRT scanners are cubical or rectangular-cubical space elements with edge lengths of one to two millimeters and a volume of 1 to 8 mm3.
In Mehta et al., NeuroImage 20 (2003), pages 1438 to 1454, “Evaluation of voxel-based morphometry for focal lesion detection in individuals,” a corresponding voxel-based morphometry is presented for focal lesion detection in individuals, wherein contours manually determined by an expert are used as a basis for comparison (also termed “ground truth”). A method is thereby assessed in regard to its ability to automatically determine the contours of lesions. A deficiency or deficit of white and grey substance is looked for since lesions always cause a decrease in the detected white substance or respectively grey substance where white substance or respectively grey substance should be dominant.
In Stamatakis et al., Brain and Language 94 (2005), pages 167-177, “Identifying lesions on structural brain images—Validation of the method and application to neuropsychological patients,” a variety of known segmentation or respectively categorization algorithms were applied to the MRT data of damaged brains with lesions. It was found that these do not function satisfactorily for existing lesions. It was therefore proposed to instead compare the smoothed, non-segmented MRT data of a patient's brain with the data of a control group in order to identify and mark lesions.
According to de Boer et al., NeuroImage 45 (2009), pages 1151-1161, “White matter lesion extension to automatic brain tissue segmentation on MRI,”, the insufficient suitability of known categorization algorithms in the presence of lesions was circumvented by using T2-weighted or FLAIR scans (fluid attenuated inversion recovery) in which lesions appear hyperintense in the white substance in addition to the known segmentation of T1-weighted scans. These are used to define a fourth tissue class, so-called “white matter lesions” (WML). These WML regions are no longer subjected to the known segmentation into WM, GM and CSF.
U.S. Pat. No. 6,366,797 B1 discloses a method for analyzing medical data, particularly MRT scans. A volume of the brain is determined that excludes liquor-filled regions. The brain volume is normalized with reference to a full contour volume so that a parenchymal fraction of the brain is generated. This serves as a measure of cerebral atrophy and helps determine the severity and progression of multiple sclerosis or other clinical pictures that lead to neurodegeneration or axonal damage.
U.S. Pat. No. 8,112,144 B2 relates to a method for visualizing cerebral atrophies. Asymmetries are exploited that result from cerebral atrophies arising on one side. The relations of the grey substance and white substance in the left and corresponding right hemispheres of the brain are compared with each other, and statistically significant differences are visualized.
The aforementioned methods are able to identify lesions or atrophies of the brain to varying degrees.